A rotation-invariant algorithm based on binary circular filters is developed for optical pattern recognition. The features of the genetic algorithm provide a highly efficient and rapid learning process. During training, the parameters of a circular filter are selected to maximize the distinction between the target and other expected objects in the image. These iteratively designed filters are good discriminators because they utilize all the spatial visual information about the target. Filters that optimize the trade-off between noise robustness and sharpness of the correlation peak can be determined. Binary circular filters when combined with spatial light modulators are appropriate for real-time applications.